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Feature Importance

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하위 태스크

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평가표

6

보존된 평가 기록

12

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/harris-chris/joint-shapley-values

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Joint Shapley values: a measure of joint feature importance

https://github.com/harvard-ophthalmology-ai-lab/fairdomain

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FairDomain: Achieving Fairness in Cross-Domain Medical Image Segmentation and Classification

https://github.com/hcguoO0/FIA

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Feature Importance-aware Transferable Adversarial Attacks

https://github.com/hcmlab/GANterfactual

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GANterfactual - Counterfactual Explanations for Medical Non-Experts using Generative Adversarial Learning

https://github.com/henningzakrisson/local-glm-boost

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A tree-based varying coefficient model

https://github.com/heorhiis/fimba-attack

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FIMBA: Evaluating the Robustness of AI in Genomics via Feature Importance Adversarial Attacks

https://github.com/hiaac-finance/xai_aggregation

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Multi-criteria Rank-based Aggregation for Explainable AI

https://github.com/holistic-ai/holisticai-research

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Evaluating Explainability in Machine Learning Predictions through Explainer-Agnostic Metrics

https://github.com/hqyyqh888/RobustSemanComm

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Robust Semantic Communications with Masked VQ-VAE Enabled Codebook

https://github.com/hugozhl/cafe

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CAFE: Towards Compact, Adaptive, and Fast Embedding for Large-scale Recommendation Models

https://github.com/hydroml/umfi

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Ultra-marginal Feature Importance: Learning from Data with Causal Guarantees

https://github.com/iamollas/Altruist

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Altruist: Argumentative Explanations through Local Interpretations of Predictive Models

https://github.com/iamtheo2000/musiclime

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MusicLIME: Explainable Multimodal Music Understanding

https://github.com/iancovert/sage

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Understanding Global Feature Contributions With Additive Importance Measures

https://github.com/ibm/torchlogic

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Neural Reasoning Networks: Efficient Interpretable Neural Networks With Automatic Textual Explanations

https://github.com/iiis-li-group/openfe

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OpenFE: Automated Feature Generation with Expert-level Performance

https://github.com/imatge-upc/survlimepy

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SurvLIMEpy: A Python package implementing SurvLIME

https://github.com/ink-usc/dig

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Discretized Integrated Gradients for Explaining Language Models

https://github.com/inseq-team/inseq

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Inseq: An Interpretability Toolkit for Sequence Generation Models

https://github.com/intelligence-csd-auth-gr/LionLearn/tree/master/LioNets_V2

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LioNets: A Neural-Specific Local Interpretation Technique Exploiting Penultimate Layer Information

https://github.com/jacobdineen/hiccs2021

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Unified Explanations in Machine Learning Models: A Perturbation Approach

https://github.com/jeremy-goldwasser/feature-rankings

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Statistical Significance of Feature Importance Rankings

https://github.com/jeremy-goldwasser/rankshap

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Statistical Significance of Feature Importance Rankings

https://github.com/jgethanchen/gaia-ood

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GAIA: Delving into Gradient-based Attribution Abnormality for Out-of-distribution Detection

https://github.com/jieqin-ai/amr

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Activation Modulation and Recalibration Scheme for Weakly Supervised Semantic Segmentation

https://github.com/jimmafeni/efi-toolbox

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EFI: A Toolbox for Feature Importance Fusion and Interpretation in Python

https://github.com/jkarolczak/important-parts-of-prototypes

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This part looks alike this: identifying important parts of explained instances and prototypes

https://github.com/jonathancrabbe/cars

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Concept Activation Regions: A Generalized Framework For Concept-Based Explanations

https://github.com/joris-pries/bp-feature-importance

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The Berkelmans-Pries Feature Importance Method: A Generic Measure of Informativeness of Features

https://github.com/jornjan/mmshap-xai2024

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Feature importance to explain multimodal prediction models. A clinical use case

https://github.com/jpmorganchase/cf-shap

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Counterfactual Shapley Additive Explanations

https://github.com/juntang-zhuang/explain_invertible

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Decision Explanation and Feature Importance for Invertible Networks

https://github.com/kairawal/evaluating-model-explanations-without-ground-truth

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Evaluating Model Explanations without Ground Truth

https://github.com/karim-53/compare-xai

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Do We Need Another Explainable AI Method? Toward Unifying Post-hoc XAI Evaluation Methods into an Interactive and Multi-dimensional Benchmark

https://github.com/kbunte/lvq_toolbox

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Detection of extragalactic Ultra-Compact Dwarfs and Globular Clusters using Explainable AI techniques

https://github.com/khairulislam/Timeseries-Explained

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WinTSR: A Windowed Temporal Saliency Rescaling Method for Interpreting Time Series Deep Learning Models

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